A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Wireless energy harvesting for Internet of Things

Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead

Real-time and long lasting Internet of Things through semantic wake-up radios

The world is going towards the Internet of Things (IoT) where trillions of objects that are common in our lives will be enhanced and revolutionized by adding them computational and networking capabilities. Examples are cars, street lamps, industrial machinery, electrical appliances. The corner- stone of Internet of Things research is Wireless Sensor Networks (WSNs). These networks are made of hundreds of low-cost, low-complexity devices endowed with sensors to monitor the surrounding environment or objects. Typically these devices (also called sensors, nodes or motes) are battery-powered, therefore they can operate for a limited amount of time (i.e., days) before running out of energy. This is the main challenge that applications of Wireless Sensor Networks have to face. Since one of the major power consumers in a node is the radio transceiver, a lot of research effort has been put into finding solutions that keep the radio in a low-power state as much as possible, while not harming the communication capability. While this approach brings the network lifetime, i.e. the time before battery-operated nodes die having depleted their energy, to years or more, it introduces significant latency, as the energy reduction comes at the cost of not being able to reach nodes in deep sleep for long period of times. The most promising solution to this problem is the wake-up radio, an additional ultra-low power transceiver used for the sole purpose of triggering the activation of the high power, high bandwidth radio. Wake-up radio enabled IoT systems maintain always on their wake up radio, which has a negligible energy consumption, in this way optimizing both energy and latency performance metrics. Most of the research so far focused on the design of wake-up receivers, while a limited amount of communication protocols that take advantage of this radio has been proposed. Moreover, almost all of these protocols have been evaluated only through simulations. In this thesis we set to start filling this gap. We first evaluate the range performance of an ultra-low power wake-up receiver integrated into a state- of-the-art Wireless Sensor Network mote, the MagoNode++. Based on the results of this evaluation we deploy an outdoor testbed made of MagoNode++ motes. The testbed allows to validate in a real-world scenario our implementation of CTP-WUR, an extension of the widely used Collection Tree Protocol (CTP) for wake-up radio-enabled Wireless Sensor Networks. The comparison between CTP-WUR and CTP demonstrates that wake-up radios can effectively reduce the power consumption and obtain, at the same time, end-to-end latencies in the order of milliseconds, enabling new time critical applications. Based on the results and on the insights gained dur- ing the testbed evaluation a new version of CTP-WUR is presented that improves its performance across all the metrics taken into consideration: end-to-end packet latency, energy consumption and Packet Delivery Ratio

Building a green connected future: smart (Internet of) Things for smart networks

The vision of Internet of Things (IoT) promises to reshape society by creating a future where we will be surrounded by a smart environment that is constantly aware of the users and has the ability to adapt to any changes. In the IoT, a huge variety of smart devices is interconnected to form a network of distributed agents that continuously share and process information. This communication paradigm has been recognized as one of the key enablers of the rapidly emerging applications that make up the fabric of the IoT. These networks, often called wireless sensor networks (WSNs), are characterized by the low cost of their components, their pervasive connectivity, and their self-organization features, which allow them to cooperate with other IoT elements to create large-scale heterogeneous information systems. However, a number of considerable challenges is arising when considering the design of large-scale WSNs. In particular, these networks are made up by embedded devices that suffer from severe power constraints and limited resources. The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs. Green wireless networks are considered a fundamental vehicle for enabling all those critical IoT applications where devices, for different reasons, do not carry batteries, and that therefore only harvest energy and store it for future use. These networks are considered to have the potential of infinite lifetime since they do not depend on batteries, or on any other limited power sources. Wake-up radios, coupled with energy provisioning techniques, further assist on overcoming the physical constraints of traditional WSNs. In addition, they are particularly important in green WSNs scenarios in which it is difficult to achieve energy neutrality due to limited harvesting rates. In this PhD thesis we set to investigate how different data forwarding mechanisms can make the most of these green wireless networks-enabling technologies, namely, energy harvesting and wake-up radios. Specifically, we present a number of cross-layer routing approaches with different forwarding design choices and study their consequences on network performance. Among the most promising protocol design techniques, the past decade has shown the increasingly intensive adoption of techniques based on various forms of machine learning to increase and optimize the performance of WSNs. However, learning techniques can suffer from high computational costs as nodes drain a considerable percentage of their energy budget to run sophisticated software procedures, predict accurate information and determine optimal decision. This thesis addresses also the problem of local computational requirements of learning-based data forwarding strategies by investigating their impact on the performance of the network. Results indicate that local computation can be a major source of energy consumption; it’s impact on network performance should not be neglected

MG-leach: an enhanced leach protocol for wireless sensor network

A wireless sensor network is made up of a large number of small sensor nodes with limited energy resources, which is a real problem for this network. In this article, we will study the ingestion of node energy in these networks at the routing level. In addition, we are modifying one of the most popular routing algorithms for data communication in the WSN: LEACH (Adaptive Hierarchy with Low Power Consumption). The modified version of the LEACH base version "MG_LEACH" uses an intermediate cluster header to transmit data, extend the network lifetime and send more data than the original protocol. Our proposed algorithm is simulated using MATLAB to verify the effectiveness of improving the lifetime of this network. The results of the simulation confirmed that the system was working better than the LEACH basic system and that the network life had been improved.